AWS Certified AI Practitioner (AIF-C01) Cert Prep

Course details

The AWS Certified AI Practitioner (AIF-C01) exam is intended for individuals who can effectively demonstrate overall knowledge of artificial intelligence and machine learning, generative AI technologies, and associated AWS services and tools, independent of a specific job role. Check out this course to prepare for the exam, which till test your knowledge of: AI, ML, and generative AI concepts, methods, and strategies in general and on AWS; the appropriate use of AI/ML and generative AI technologies to ask relevant questions within your organization; the correct types of AI/ML technologies to apply to specific use cases; and using AI, ML, and generative AI technologies responsibly.

Concepts

0. Introduction

01 - AWS Certified AI Practitioner (AIF-C01) - Introduction

1. Exam Guide

02 - Module 1 - Exam foundation introduction
03 - Learning objectives
04 - Introduction
05 - Target candidate description
06 - Exam content
07 - Exam question domains

2. Basic AI Concepts

08 - Module 2 - Fundamentals of AI and ML introduction
09 - Learning objectives
10 - Basic AI terminology
11 - Introduction to machine learning
12 - Introduction to deep learning
13 - Question breakdown, part 1
14 - Question breakdown, part 2

3. Practical Use Cases for AI

15 - Learning objectives
16 - AI patterns and anti-patterns
17 - ML techniques
18 - Real-world AI applications
19 - AWS-managed AI ML services
20 - Question breakdown, part 1
21 - Question breakdown, part 2

4. ML Development Lifecycle

22 - Learning objectives
23 - ML pipeline components
24 - ML model sources and deployment types
25 - Introduction to MLOps
26 - AWS ML pipeline services
27 - ML model performance metrics
28 - Question breakdown, part 1
29 - Question breakdown, part 2

5. Basic Concepts of Generative AI

30 - Module 3 - Fundamentals of generative AI introduction
31 - Learning objectives
32 - Basic generative AI terminology
33 - Generative AI use cases
34 - Foundation model lifecycle
35 - Question breakdown, part 1
36 - Question breakdown, part 2

6. Generative AI Capabilities and Limitations

37 - Learning objectives
38 - Generative AI advantages
39 - Generative AI disadvantages
40 - Model selection decision tree
41 - Generative AI business value and metrics
42 - Question breakdown, part 1
43 - Question breakdown, part 2

7. AWS Generative AI Offerings

44 - Learning objectives
45 - AWS generative AI services and features
46 - AWS generative AI advantages and benefits
47 - AWS generative AI cost tradeoffs
48 - Question breakdown, part 1
49 - Question breakdown, part 2

8. Foundation Model Design

50 - Module 4 - Applications of foundation models introduction
51 - Learning objectives
52 - Pretrained model selection criteria
53 - Model inference parameters
54 - Introduction to RAG
55 - Introduction to vector databases
56 - AWS vector database service
57 - Foundation model customization cost tradeoffs
58 - Generative AI agents
59 - Question breakdown, part 1
60 - Question breakdown, part 2

9. Foundation Model Performance

61 - Learning objectives
62 - Foundation model performance metrics and evaluation
63 - Foundation model business objective criteria
64 - Question breakdown, part 1
65 - Question breakdown, part 2

10. Foundation Model Training and Fine-Tuning

66 - Learning objectives
67 - Foundation model training
68 - Foundation model fine-tuning
69 - Foundation model data preparation
70 - Question breakdown, part 1
71 - Question breakdown, part 2

11. Prompt Engineering

72 - Learning objectives
73 - Prompt workflow
74 - Prompt engineering concepts
75 - Prompt engineering techniques
76 - Prompt engineering best practices
77 - Prompt engineering risks and limitations
78 - Question breakdown, part 1
79 - Question breakdown, part 2

12. Responsible AI System Development

80 - Module 5 - Responsible and secure AI solutions introduction
81 - Learning objectives
82 - Responsible AI features
83 - AWS responsible AI tools
84 - Responsible AI model selection practices
85 - Generative AI legal risks
86 - AI dataset characteristics
87 - AI bias and variance
88 - AWS AI bias detection tools
89 - Question breakdown, part 1
90 - Question breakdown, part 2

13. Transparent and Explainable AI Models

91 - Learning objectives
92 - Transparency and explainability definitions
93 - AWS transparency and explainability tools
94 - AI model safety and transparency tradeoffs
95 - Human-centered AI design principles
96 - Question breakdown, part 1
97 - Question breakdown, part 2

14. AI Security

98 - Learning objectives
99 - AWS AI security services and features
100 - Data citations and origin documentation
101 - Secure data engineering best practices
102 - AI security and privacy considerations
103 - Question breakdown, part 1
104 - Question breakdown, part 2

15. AI Governance and Compliance

105 - Learning objectives
106 - AWS governance and compliance services
107 - Data governance strategies
108 - Governance protocols and compliance standards
109 - Question breakdown, part 1
110 - Question breakdown, part 2

Conclusion

111 - AWS Certified AI Practitioner (AIF-C01) - Summary